Thursday, January 19, 2006

Desperate Data Warehouses

A Gartner Group report mentions that at least 50% of data warehouse projects fail. Of course on its own this sounds bad, but just how bad is it, and what is meant by failure e.g. is being one month late failure, or does it mean complete failure to deliver? How do IT projects in general do? Standish Group run a fairly exacting survey which in 2003 covered 13,522 IT projects, a very large sample indeed. Of these just 34% were an "unqualified success". Complete failure to deliver were just 15%. The rest are in the middle i.e. they delivered but were not perceived to be complete successes in some way. To be precise: 51% had "cost overruns, time overruns, and projects not delivered with the right functionality to support the business". Unfortunately the Gartner note does not define "failure" as precisely as Standish; they define the "over 50% as being "limited acceptance or be outright failures". It is also unclear whether the Gartner figure was a prediction based on hard data, or the opinion of one or more of their analysts.

The Standish study usefully splits the success rate by project size, with a miserable 2% of projects larger than USD 10M in size being complete successes, with 46% of projects below USD 750k being complete successes, 32% up to USD 3M and, 23% at USD 3-6M and 11% at USD 6-10M. The average data warehouse project is somewhere around the USD 2-5M range, with USD 3M often quoted, so indeed on this basis it would seem we should only expect around 25% or so to be "unqualified successes". Unfortunately I don't have data available for the failure rate split by size, which presumably may follow a similar pattern, and the rather loose definition that Gartner use makes it hard to compare like with like.

Even if turns out that data warehouse projects aren't any (or at least much) worse than other IT projects, this is not a great advert for the IT industry. The Standish data most certainly gives a clear message that if you can possible reduce the scope of a project to smaller, bite-sized projects, then you greatly enhance your chance of success. It has long been known that IT productivity drops as projects get larger. This is due to human nature - the more people you have to work with, the more communication is needed, the more complex things become, and the more chance of things being misunderstood or overlooked.

It is interesting that even very large data warehouse projects can be effectively managed in bite-sized chunks, at least if you use a federated approach rather than trying to stuff the entire enterprise's data into a single warehouse. Projects at BP, Unilever, Philips, Shell and others have taken a country by country approach, or a business line by business line approach, with individual warehouses feeding up to either regional ones or a global one, or indeed both. In this case each project becomes a fairly modest implementation, but there my be many of them. The Shell OP MIS project involved 66 separate country implementations, three regional and one global. Overall a USD 50M project, but broken down into lots of manageable, repeatable pieces.

So, if you data warehouse project is not to become desperate, think carefully about a federated architecture rather than big bang. This may not always be possible, but you will have a greater chance of success.


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